Before you touch agents or RAG pipelines, you need to understand one pattern. Everything else builds on it.

The problem LangChain solves

Most LLM applications share the same structural patterns — connect a prompt to a model, parse the output, pass it somewhere else. Without a framework, you end up reinventing that plumbing on every project.

LangChain standardizes those patterns. It gives you modular components that work across different model providers — OpenAI, Anthropic, Gemini — and slot together predictably. The key is understanding how they slot together.

One pattern to rule them all

Everything in LangChain follows a single pattern:

Input → Runnable → Output

Every component — prompt templates, models, retrievers, output parsers, chains — is a Runnable. And because they all share the same interface, they can be chained together using the pipe operator | .

This is called LCEL — LangChain Expression Language.…